Central Server Free Federated Learning over Single-sided Trust Social Networks

State-of-the-art federated learning adopts the centralized network architecture where a centralized node collects the gradients sent from child agents to update the global model. Despite its simplicity, the centralized method suffers from communication and computational bottlenecks in the central node, especially for federated learning, where a large number of clients are usually involved. Moreover, to prevent reverse engineering of the user's identity, a certain amount of noise must be added to the gradient to protect user privacy, which partially sacrifices the efficiency and the accuracy (Shokri and Shmatikov, 2015). To further protect the data privacy and avoid the communication bottleneck, the decentralized architecture has been recently proposed (Vanhaesebrouck et al., 2017; Bellet et al., 2018), where the centralized node has been removed, and each node only communicates with its neighbors (with mutual trust) by exchanging their local models. Exchanging local models is usually favored with respect to the data privacy protection over sending private gradients because the local model is the aggregation or mixture of quite a large amount of data while the local gradient directly reflects only one or a batch of private data samples. Although advantages of decentralized architecture have been well recognized over the state-of-the-art method (its centralized counterpart), it usually can only be run on the network with mutual trusts . That is, two nodes (or users) can exchange their local models only if they trust each other reciprocally (e.g.